aiqbench analysis
This research introduces Visual Saliency Steering Distillation (VSSD), a technique designed to enhance multimodal chain-of-thought reasoning by preserving subtle cross-modal distinctions that are often lost during fusion in smaller models. By leveraging attention maps and singular value decomposition, the method generates perturbed images to guide inter-layer distillation, resulting in improved rationale generation on benchmarks like ScienceQA. The approach addresses critical limitations in current vision-language integration where distinct inputs become indistinguishable after processing.
Visual Saliency Steering Distillation for Multimodal Chain-of-Thought Reasoning
arXiv:2607.22013v1 Announce Type: cross Abstract: Multimodal chain-of-thought (CoT) reasoning integrates visual and textual cues through step-by-step inference. In small models with limited token budgets, modality-interaction fusion often suppresses tiny cross-modal differences. In particular, multimodal CoT often struggles when different images pair with identical text or different texts pair with an identical image, making such inputs nearly indistinguishable after fusion. This study proposes Visual Saliency Steering Distillation (VSSD). VSSD leverages the attention maps of multimodal large language models to generate perturbed images that capture task-sensitive feature directions, and then applies singular value decomposition to extract dominant steering vectors to guide inter-layer distillation. Experiments on ScienceQA and M$^3$CoT demonstrate that VSSD improves rationale generation and answer inference. The code is available at https://github.com/BGWH123/VSSD.
Abstract:Multimodal chain-of-thought (CoT) reasoning integrates visual and textual cues through step-by-step inference. In small models with limited token budgets, modality-interaction fusion often suppresses tiny cross-modal differences. In particular, multimodal CoT often struggles when different images pair with identical text or different texts pair with an identical image, making such inputs nearly indistinguishable after fusion. This study proposes Visual Saliency Steering Distillation (VSSD). VSSD leverages the attention maps of multimodal large language models to generate perturbed images that capture task-sensitive feature directions, and then applies singular value decomposition to extract dominant steering vectors to guide inter-layer distillation. Experiments on ScienceQA and M$^3$CoT demonstrate that VSSD improves rationale generation and answer inference. The code is available at this https URL.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2607.22013 [cs.CV]
(or arXiv:2607.22013v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/ICASSP55912.2026.11463006 Focus to learn more DOI(s) linking to related resources
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